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design, simulate, and test multisensor tracking and positioning systems

sensor fusion and tracking toolbox™ includes algorithms and tools for designing, simulating, and testing systems that fuse data from multiple sensors to maintain situational awareness and localization. reference examples provide a starting point for multi-object tracking and sensor fusion development for surveillance and autonomous systems, including airborne, spaceborne, ground-based, shipborne, and underwater systems.

you can fuse data from real-world sensors, including active and passive radar, sonar, lidar, eo/ir, imu, and gps. you can also generate synthetic data from virtual sensors to test your algorithms under different scenarios. the toolbox includes multi-object trackers and estimation filters for evaluating architectures that combine grid-level, detection-level, and object- or track-level fusion. it also provides metrics, including ospa and gospa, for validating performance against ground truth scenes.

for simulation acceleration or rapid prototyping, the toolbox supports c and c code generation.

tutorials

toolbox conventions

learn about toolbox conventions for spatial representation and coordinate systems.

tracking scenario and sensors

tracking simulation overview
you can build a complete tracking simulation using the functions and objects supplied in this toolbox.
simulate target detections by radar sensors.

inertial sensor fusion

model imu, gps, and ins/gps
model combinations of inertial sensors and gps.

estimation filters

introduction to estimation filters
general review of estimation filters provided in the toolbox.

multi-object tracking

introduction to multiple target tracking
introduction to assignment-based multiple target trackers.

metrics and visualization

introduction to tracking metrics
while designing a multi-object tracking system, it is essential to devise a method to evaluate its performance against the available ground truth.
this example shows how to use the theaterplot object to visualize various aspects of a tracking scenario.

featured examples

scanning radar mode configuration

model different radar scan modes using the fusionradarsensor. this example shows how to configure the fusionradarsensor for several commonly used radar scan modes. with this model, you can simulate radars which mechanically scan, electronically scan, and which use both mechanical and electronic scanning. the scan limits in azimuth and elevation are configurable for both mechanical and electronic scan modes.

estimate orientation through inertial sensor fusion

use 6-axis and 9-axis fusion algorithms to compute orientation. there are several algorithms to compute orientation from inertial measurement units (imus) and magnetic-angular rate-gravity (marg) units. this example covers the basics of orientation and how to use these algorithms.

tracking maneuvering targets

track maneuvering targets using various tracking filters. the example shows the difference between filters that use a single motion model and multiple motion models.

convert detections to objectdetection format

these examples show how to convert actual detections in the native format of the sensor into objectdetection objects. objectdetection is the standard input format for most tracking filters and trackers in the toolbox. the six examples progressively show how to set up objectdetection with varied tracking scenarios.

tuning a multi-object tracker

tune and run a tracker to track multiple objects in the scene. the example explains and demonstrates the importance of key properties of the trackers in the sensor fusion and tracking toolbox.

videos

an overview of what sensor fusion is and how it helps in the design of autonomous systems.

use magnetometer, accelerometer, and gyro to estimate an object’s orientation.

use gps and an imu to estimate an object’s orientation and position.

track a single object by estimating state with an interacting multiple model filter.

introduce two common problems in multi object tracking: data association and track maintenance.

introduce track-to-track fusion and tracking architecture.